Introduction to Fuel Consumption Optimization Techniques

  • Aydin AziziEmail author
  • Poorya Ghafoorpoor Yazdi
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


Efforts to optimize fuel consumption have driven and inspired various industries, including the automobile industry, to create a wealth of new inventions and technologies. Since the issue of global warming was brought into the spotlight, the mechanics of the automobile industry have evolved rapidly, due to the greenhouse gas emissions produced by internal combustion engines. The advancement of technology within the power industry has helped in reducing fuel consumption, as well as in the reduction of greenhouse gas emissions. This chapter aims to introduce factors effecting fuel consumption and related optimizing methods with focusing on vehicle fuel consumption. 


  1. 1.
    M.I. Hoffert et al., Advanced technology paths to global climate stability: energy for a greenhouse planet. Science 298(5595), 981–987 (2002)CrossRefGoogle Scholar
  2. 2.
    P.M. Vitousek, H.A. Mooney, J. Lubchenco, J.M. Melillo, Human domination of Earth’s ecosystems. Science 277(5325), 494–499 (1997)CrossRefGoogle Scholar
  3. 3.
    B. Dong, R.T. Sutton, A.A. Scaife, Multidecadal modulation of El Nino–Southern Oscillation (ENSO) variance by Atlantic Ocean sea surface temperatures. Geophys. Res Lett. 33(8) 2006Google Scholar
  4. 4.
    M. Younger, H.R. Morrow-Almeida, S.M. Vindigni, A.L. Dannenberg, The built environment, climate change, and health: opportunities for co-benefits. Am. J. Prev. Med. 35(5), 517–526 (2008)CrossRefGoogle Scholar
  5. 5.
    S. Pacala, R. Socolow, Stabilization wedges: solving the climate problem for the next 50 years with current technologies. Science 305(5686), 968–972 (2004)CrossRefGoogle Scholar
  6. 6.
    S. Shafiee, E. Topal, When will fossil fuel reserves be diminished? Energy Policy 37(1), 181–189 (2009)CrossRefGoogle Scholar
  7. 7.
    I. Dincer, Renewable energy and sustainable development: a crucial review. Renew. Sustain. Energy Rev. 4(2), 157–175 (2000)CrossRefGoogle Scholar
  8. 8.
    K.A. Small, K. Van Dender, Fuel efficiency and motor vehicle travel: the declining rebound effect. Energy J., 25–51 (2007)Google Scholar
  9. 9.
    P.K. Goldberg, The effects of the corporate average fuel efficiency standards in the US. J Ind. Econ. 46(1), 1–33 (1998)MathSciNetCrossRefGoogle Scholar
  10. 10.
    R. Stone, Motor Vehicle Fuel Economy (Macmillan International Higher Education, 2017)Google Scholar
  11. 11.
    P. Mock, J. German, A. Bandivadekar, I. Riemersma, Discrepancies between type-approval and “real-world” fuel-consumption and CO. Int. Counc. Clean Transp. 13 (2012)Google Scholar
  12. 12.
    P. Kågeson, Reducing CO2 emissions from new cars. Eur. Fed. Transp. Environ. (2005)Google Scholar
  13. 13.
    S. McBeath, Competition Car Aerodynamics, 3rd edn. (Veloce Publishing Ltd, 2017)Google Scholar
  14. 14.
    K. Holmberg, P. Andersson, N.-O. Nylund, K. Mäkelä, A. Erdemir, Global energy consumption due to friction in trucks and buses. Tribol. Int. 78, 94–114 (2014)CrossRefGoogle Scholar
  15. 15.
    J. Liu et al., Nanoparticle chemically end-linking elastomer network with super-low hysteresis loss for fuel-saving automobile. Nano Energy 28, 87–96 (2016)CrossRefGoogle Scholar
  16. 16.
    A.-H. Kakaee, P. Rahnama, A. Paykani, Influence of fuel composition on combustion and emissions characteristics of natural gas/diesel RCCI engine. J. Nat. Gas Sci. Eng. 25, 58–65 (2015)CrossRefGoogle Scholar
  17. 17.
    A. Dicks, D.A.J. Rand, Fuel cell Systems Explained (Wiley Online Library, 2018)Google Scholar
  18. 18.
    E. Khalife, M. Tabatabaei, A. Demirbas, M. Aghbashlo, Impacts of additives on performance and emission characteristics of diesel engines during steady state operation. Prog. Energy Combust. Sci. 59, 32–78 (2017)CrossRefGoogle Scholar
  19. 19.
    M. Zhou, H. Jin, W. Wang, A review of vehicle fuel consumption models to evaluate eco-driving and eco-routing. Transp. Res. Part D: Transp. Environ. 49, 203–218 (2016)CrossRefGoogle Scholar
  20. 20.
    M. Flannigan et al., Fuel moisture sensitivity to temperature and precipitation: climate change implications. Clim. Change 134(1–2), 59–71 (2016)CrossRefGoogle Scholar
  21. 21.
    Y. Xu, F.E. Gbologah, D.-Y. Lee, H. Liu, M.O. Rodgers, R.L. Guensler, Assessment of alternative fuel and powertrain transit bus options using real-world operations data: life-cycle fuel and emissions modeling. Appl. Energy 154, 143–159 (2015)CrossRefGoogle Scholar
  22. 22.
    L. Li, S. You, C. Yang, B. Yan, J. Song, Z. Chen, Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses. Appl. Energy 162, 868–879 (2016)CrossRefGoogle Scholar
  23. 23.
    H. Wang, X. Zhang, M. Ouyang, Energy consumption of electric vehicles based on real-world driving patterns: a case study of Beijing. Appl. Energy 157, 710–719 (2015)CrossRefGoogle Scholar
  24. 24.
    S.E. Li, H. Peng, Strategies to minimize the fuel consumption of passenger cars during car-following scenarios. Proc. Inst. Mech. Eng., Part D: J. Automobile Eng. 226(3), 419–429 (2012)CrossRefGoogle Scholar
  25. 25.
    L. DeRaad, The influence of road surface texture on tire rolling resistance. SAE Technical Paper0148-7191, 1978Google Scholar
  26. 26.
    G. Descornet, Road-surface influence on tire rolling resistance, in Surface characteristics of roadways: international research and technologies (ASTM International, 1990)Google Scholar
  27. 27.
    U. Sandberg, A. Bergiers, J.A. Ejsmont, L. Goubert, R. Karlsson, M. Zöller, Road Surface Influence on Tyre/Road Rolling Resistance (MIRIAM, editor, 2011)Google Scholar
  28. 28.
    I. Zaabar, K. Chatti, A Field Investigation of the Effect of Pavement Surface Conditions on Fuel Consumption (2011)Google Scholar
  29. 29.
    F. Perrotta, L. Trupia, T. Parry, L.C. Neves, Route level analysis of road pavement surface condition and truck fleet fuel consumption, in Pavement Life-Cycle Assessment (CRC Press, 2017), pp. 61–68Google Scholar
  30. 30.
    N. Dhakal, M.A. Elseifi, Effects of asphalt-mixture characteristics and vehicle speed on fuel-consumption excess using finite-element modeling. J. Transp. Eng., Part A: Syst. 143(9), 04017047 (2017)CrossRefGoogle Scholar
  31. 31.
    M. Ziyadi, H. Ozer, S. Kang, I.L. Al-Qadi, Vehicle energy consumption and an environmental impact calculation model for the transportation infrastructure systems. J. Clean. Prod. 174, 424–436 (2018)CrossRefGoogle Scholar
  32. 32.
    A. Loulizi, H. Rakha, Y. Bichiou, Quantifying grade effects on vehicle fuel consumption for use in sustainable highway design. Int. J. Sustain. Transp. 12(6), 441–451 (2018)CrossRefGoogle Scholar
  33. 33.
    Y. Huang, E.C. Ng, J.L. Zhou, N.C. Surawski, E.F. Chan, G. Hong, Eco-driving technology for sustainable road transport: a review. Renew. Sustain. Energy Rev. 93, 596–609 (2018)CrossRefGoogle Scholar
  34. 34.
    M. Speckert, M. Lübke, B. Wagner, T. Anstötz, C. Haupt, Representative road selection and route planning for commercial vehicle development, in Commercial Vehicle Technology 2018 (Springer, 2018), pp. 117–128Google Scholar
  35. 35.
    A.M. Pérez-Zuriaga, D. Llopis-Castelló, F.J. Camacho-Torregrosa, I. Belkacem, A. García, Impact of Horizontal Geometric Design of Two-Lane Rural Roads on Vehicle CO2 Emissions (2017)Google Scholar
  36. 36.
    L. Liu, C. Li, X. Hua, Y. Li, Multi-factor integration based eco-driving optimization of vehicles with same driving characteristics, in Chinese Automation Congress (CAC) (IEEE, 2017), pp. 6871–6876Google Scholar
  37. 37.
    J. Robson, C. Dodds, Stochastic road inputs and vehicle response. Veh. Syst. Dyn. 5(1–2), 1–13 (1976)CrossRefGoogle Scholar
  38. 38.
    A. Azizi, Computer-based analysis of the stochastic stability of mechanical structures driven by white and colored noise. Sustainability 10(10), 3419 (2018)CrossRefGoogle Scholar
  39. 39.
    J. Palmer, S. Sljivar, Vehicle Fuel Consumption Monitor and Feedback Systems (ed. Google Patents, 2017)Google Scholar
  40. 40.
    P.J. Alvarado, Steel vs. plastics: the competition for light-vehicle fuel tanks. JOM 48(7), 22–25 (1996)CrossRefGoogle Scholar
  41. 41.
    Y. Kurihara, K. Nakazawa, K. Ohashi, S. Momoo, K. Numazaki, Development of multi-layer plastic fuel tanks for Nissan research vehicle-II. SAE Transa. 1239–1245 (1987)Google Scholar
  42. 42.
    G. Bahng, D. Jang, Y. Kim, M. Shin, A new technology to overcome the limits of HCCI engine through fuel modification. Appl. Therm. Eng. 98, 810–815 (2016)CrossRefGoogle Scholar
  43. 43.
    B. Erkuş, M.I. Karamangil, A. Sürmen, Enhancing the heavy load performance of a gasoline engine converted for LPG use by modifying the ignition timings. Appl. Therm. Eng. 85, 188–194 (2015)CrossRefGoogle Scholar
  44. 44.
    S. Tangöz, S.O. Akansu, N. Kahraman, Y. Malkoc, Effects of compression ratio on performance and emissions of a modified diesel engine fueled by HCNG. Int. J Hydrogen Energy 40(44), 15374–15380 (2015)CrossRefGoogle Scholar
  45. 45.
    M. Ben-Chaim, E. Shmerling, A. Kuperman, Analytic modeling of vehicle fuel consumption. Energies 6(1), 117–127 (2013)CrossRefGoogle Scholar
  46. 46.
    K. Ahn, Microscopic Fuel Consumption and Emission Modeling (Virginia Tech, 1998)Google Scholar
  47. 47.
    M. Ross, Automobile fuel consumption and emissions: effects of vehicle and driving characteristics. Annu. Rev. Energy Env. 19(1), 75–112 (1994)MathSciNetCrossRefGoogle Scholar
  48. 48.
    R. Smit, A. Brown, Y. Chan, Do air pollution emissions and fuel consumption models for roadways include the effects of congestion in the roadway traffic flow? Environ. Model Softw. 23(10–11), 1262–1270 (2008)CrossRefGoogle Scholar
  49. 49.
    H.A. Rakha, K. Ahn, K. Moran, B. Saerens, E. Van den Bulck, Virginia tech comprehensive power-based fuel consumption model: model development and testing. Transp. Res. Part D: Transp. Environ. 16(7), 492–503 (2011)CrossRefGoogle Scholar
  50. 50.
    T.-Q. Tang, H.-J. Huang, H.-Y. Shang, Influences of the driver’s bounded rationality on micro driving behavior, fuel consumption and emissions. Transp. Res. Part D: Transp. Environ. 41, 423–432 (2015)CrossRefGoogle Scholar
  51. 51.
    J. Wang, H.A. Rakha, Fuel consumption model for heavy duty diesel trucks: Model development and testing. Transp. Res. Part D: Transp. Environ. 55, 127–141 (2017)CrossRefGoogle Scholar
  52. 52.
    Y. Wang, W. Zhao, G. Zhou, Q. Gao, C. Wang, Suspension mechanical performance and vehicle ride comfort applying a novel jounce bumper based on negative Poisson’s ratio structure. Adv. Eng. Softw. 122, 1–12 (2018)CrossRefGoogle Scholar
  53. 53.
    W. Ren, B. Peng, J. Shen, Y. Li, Y. Yu, Study on vibration characteristics and human riding comfort of a special equipment cab. J. Sens. 2018 (2018)Google Scholar
  54. 54.
    P.B. Koganti, F.E. Udwadia, Unified approach to modeling and control of rigid multibody systems. J. Guidance, Control, Dyn. 2683–2698 (2016)CrossRefGoogle Scholar
  55. 55.
    C.M. Pappalardo, D. Guida, Control of nonlinear vibrations using the adjoint method. Meccanica 52(11–12), 2503–2526 (2017)MathSciNetzbMATHCrossRefGoogle Scholar
  56. 56.
    C.M. Pappalardo, D. Guida, Use of the adjoint method for controlling the mechanical vibrations of nonlinear systems. Machines 6(2), 19 (2018)CrossRefGoogle Scholar
  57. 57.
    Y. Huang, J. Na, X. Wu, X. Liu, Y. Guo, Adaptive control of nonlinear uncertain active suspension systems with prescribed performance. ISA Trans. 54, 145–155 (2015)CrossRefGoogle Scholar
  58. 58.
    Q. Zhu, J.-J. Ding, M.-L. Yang, LQG control based lateral active secondary and primary suspensions of high-speed train for ride quality and hunting stability. IET Control Theory Appl. 12(10), 1497–1504 (2018)MathSciNetGoogle Scholar
  59. 59.
    J. Marzbanrad, N. Zahabi, H∞ active control of a vehicle suspension system exited by harmonic and random roads. Mech. Mech. Eng. 21(1) (2017)Google Scholar
  60. 60.
    M.M. Elmadany, Optimal linear active suspensions with multivariable integral control. Veh. Syst. Dyn. 19(6), 313–329 (1990)CrossRefGoogle Scholar
  61. 61.
    H. Siswoyo, N. Mir-Nasiri, M.H. Ali, Design and development of a semi-active suspension system for a quarter car model using PI controller. J. Autom. Mobile Robot. Intell. Syst. 11 (2017)CrossRefGoogle Scholar
  62. 62.
    H. Metered, W. Abbas, A. Emam, Optimized Proportional Integral Derivative Controller of Vehicle Active Suspension System using Genetic Algorithm. SAE Technical Paper (2018), pp. 01–1399Google Scholar
  63. 63.
    H. Li, X. Jing, H.R. Karimi, Output-feedback-based $ H_ {\infty} $ control for vehicle suspension systems with control delay. IEEE Trans. Industr. Electron. 61(1), 436–446 (2014)CrossRefGoogle Scholar
  64. 64.
    A.E.-N.S. Ahmed, A.S. Ali, N.M. Ghazaly, G.A. El-Jaber, PID controller of active suspension system for a quarter car model. Int J. Adv. Eng. Technol. 8(6), 899 (2015)Google Scholar
  65. 65.
    A. Buscarino, C.F.L. Fortuna, M. Frasca, Passive and active vibrations allow self-organization in large-scale electromechanical systems. Int. J. Bifurcat. Chaos 26(07), 1650123 (2016)CrossRefGoogle Scholar
  66. 66.
    F. Zhao, S.S. Ge, F. Tu, Y. Qin, M. Dong, Adaptive neural network control for active suspension system with actuator saturation. IET Control Theory Appl. 10(14), 1696–1705 (2016)MathSciNetCrossRefGoogle Scholar
  67. 67.
    Y. Taskin, Y. Hacioglu, N. Yagiz, Experimental evaluation of a fuzzy logic controller on a quarter car test rig. J. Brazilian Soc. Mech. Sci. Eng. 39(7), 2433–2445 (2017)CrossRefGoogle Scholar
  68. 68.
    D. Singh, Modeling and control of passenger body vibrations in active quarter car system: a hybrid ANFIS PID approach. Int. J. Dyn. Control (2018)Google Scholar
  69. 69.
    M.A.Z.I.M. Fauzi et al., Enhancing Ride Comfort of Quarter Car Semi-active Suspension System Through State-Feedback Controller (Springer Singapore, Singapore, 2018), pp. 827–837Google Scholar
  70. 70.
    V. Marmarelis, Analysis of physiological systems: The white-noise approach (Springer Science & Business Media, 2012)Google Scholar
  71. 71.
    J. Hawkins Jr., S. Stevens, The masking of pure tones and of speech by white noise. J. Acoust. Soc. Am. 22(1), 6–13 (1950)CrossRefGoogle Scholar
  72. 72.
    A. Ashkzari, A. Azizi, Introducing genetic algorithm as an intelligent optimization technique, in Applied Mechanics and Materials, vol. 568. (Trans Tech Publ 2014), pp. 793–797Google Scholar
  73. 73.
    A. Azizi, Introducing a novel hybrid artificial intelligence algorithm to optimize network of industrial applications in modern manufacturing. Complexity 2017 (2017)MathSciNetCrossRefGoogle Scholar
  74. 74.
    A. Azizi, Hybrid artificial intelligence optimization technique, in Applications of Artificial Intelligence Techniques in Industry 4.0 (Springer, 2019), pp. 27–47Google Scholar
  75. 75.
    A. Azizi, Modern manufacturing, in Applications of Artificial Intelligence Techniques in Industry 4.0 (Springer, 2019), pp. 7–17Google Scholar
  76. 76.
    A. Azizi, RFID network planning, in Applications of Artificial Intelligence Techniques in Industry 4.0 (Springer, 2019), pp. 19–25Google Scholar
  77. 77.
    A. Azizi, Applications of Artificial Intelligence Techniques in Industry 4.0 (ed: Springer)Google Scholar
  78. 78.
    A. Azizi, F. Entesari, K. G. Osgouie, M. Cheragh, Intelligent mobile robot navigation in an uncertain dynamic environment, in Applied Mechanics and Materials, vol. 367. (Trans Tech Publ, 2013), pp. 388–392Google Scholar
  79. 79.
    A. Azizi, F. Entessari, K. G. Osgouie, A. R. Rashnoodi, Introducing neural networks as a computational intelligent technique, in Applied Mechanics and Materials, vol. 464. (Trans Tech Publ, 2014), pp. 369–374Google Scholar
  80. 80.
    A. Azizi, N. Seifipour, Modeling of dermal wound healing-remodeling phase by Neural Networks, in International Association of Computer Science and Information Technology-Spring Conference, 2009. IACSITSC’09, (IEEE 2009), pp. 447–450Google Scholar
  81. 81.
    A. Azizi, A. Vatankhah Barenji, M. Hashmipour, Optimizing radio frequency identification network planning through ring probabilistic logic neurons. Adv. Mech. Eng. 8(8), 1687814016663476 (2016)CrossRefGoogle Scholar
  82. 82.
    A. Azizi, P. G. Yazdi, M. Hashemipour, Interactive design of storage unit utilizing virtual reality and ergonomic framework for production optimization in manufacturing industry. Int. J. Interac. Des. Manuf. (IJIDeM) 1–9 (2018)Google Scholar
  83. 83.
    M. Koopialipoor, A. Fallah, D.J. Armaghani, A. Azizi, E.T. Mohamad, Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Eng. Comput. 1–14 (2018)Google Scholar
  84. 84.
    K.G. Osgouie, A. Azizi, Optimizing fuzzy logic controller for diabetes type I by genetic algorithm, in The 2nd International Conference on Computer and Automation Engineering (ICCAE), vol. 2. (IEEE, 2010), pp. 4–8Google Scholar
  85. 85.
    S. Rashidnejhad, A. H. Asfia, K. G. Osgouie, A. Meghdari, A. Azizi, Optimal trajectory planning for parallel robots considering time-jerk, in Applied Mechanics and Materials, vol. 390. (Trans Tech Publ, 2013), pp. 471–477Google Scholar
  86. 86.
    Y. Zhang, K. Guo, D. Wang, C. Chen, X. Li, Energy conversion mechanism and regenerative potential of vehicle suspensions. Energy 119, 961–970 (2017)CrossRefGoogle Scholar
  87. 87.
    I. Maciejewski, T. Krzyzynski, H. Meyer, Modeling and vibration control of an active horizontal seat suspension with pneumatic muscles. J. Vib. Control 1077546318763435 (2018)Google Scholar

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© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of EngineeringGerman University of Technology in OmanMuscatOman

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